Academic Jobs | Postdoctoral Fellow at the Laboratory for Innovation Science at Harvard (LISH), IQSS job at Harvard University in Cambridge, Massachusetts

Title	Postdoctoral Fellow at the Laboratory for Innovation Science at Harvard (LISH), IQSS
School	Faculty of Arts and Sciences
Department/Area	LISH / IQSS
Position Description	The Laboratory for Innovation Science at Harvard University (LISH) is accepting applications for multiple postdoctoral fellowships in quantitative social science (economics, management, psychology, and sociology). Candidates with a background in applied work with observational data establishing causal relationships, or experimental design in the lab or field settings would be well-suited. LISH is a Harvard-wide research program led by faculty co-directors Karim Lakhani and Marco Iansiti of Harvard Business School; Eva Guinan, Harvard Medical School; and David Parkes, Harvard School of Engineering and Applied Sciences. We focus on developing a science of innovation primarily through field-based experiments. The lab works with several partners (NASA, Harvard Medical School, and various other institutions and corporations) to investigate real-world innovation problems. Multiple positions available. Fellows will focus on one of four areas: Online Platforms and Contests, Science of Science, the Economics and Management of AI Systems, or Innovation for All. Online Platforms and Contests addresses issues relating to open innovation on platforms like Kaggle and Topcoder or through initiatives like the Longitude Prize and X-Prize competitions. The area also studies the market design, growth strategies, and regulation of online platforms such as Freelancer and Airbnb. Past projects have examined how different kinds of incentives and different competition rules change contributor behavior and outcomes, often informed by formal game-theoretic models. Science of Science focuses on innovation in academia. Past projects have addressed the functioning of grant evaluation procedures, the information content of citations, and the management practices of research labs. Economics and Management of AI Systems area examines the effects of the introduction of AI systems into enterprises. Projects have looked at AI applicability to supply chain logistics through field experiments and AI adoption using survey data Innovation for All focuses on how to broaden the benefits of technology to include more people in developing economies, women, and people of color. Projects include a study of the impact of FinTech on financial inclusion, field experiments aimed at identifying lost innovations targeting women’s health and public health in the developing world, and the design of platforms and online courses to foster high-growth and high-impact entrepreneurship across Africa. Depending on the area of focus, the postdoctoral fellow will collaborate with one or more LISH faculty co-directors: Karim Lakhani, Eva Guinan, David Parkes, and Marco Iansiti, and/or other LISH-affiliated faculty including: Iavor Bojinov, Chiara Farronato, Kris Ferreira, Tarun Khanna, Rem Koning, Hima Lakkaraju, Kyle Myers, Frank Nagle, Christopher Stanton, Mike Tushman, and Feng Zhu. A typical empirical project involves refining a research question, designing an experiment or survey, analyzing historical and experimental data, and writing targeted at peer reviewed social science journals. Candidates should have some familiarity with: Causal analysis and experimental design: difference-in-differences and fixed effects models, instrumental variables, power analysis, analysis of attrition and non-compliance, analysis of heterogenous effects and interference Programming and scripting knowledge suitable for processing raw data for analysis (e.g., text manipulation with python); One or more computational environments for statistical analysis (e.g., MATLAB, R, or Stata). Machine learning and survey design LISH is looking for candidates with diverse backgrounds and/or new perspectives. There are no teaching requirements for these open positions. Salary is competitive. Appointment Details: This is a one-year term appointment through Harvard University with the possibility of renewal based on performance and funding. Relocation funding not provided. Two Postdoctoral Fellow positions will be funded by awards administered by the Institute for Quantitative Social Science (IQSS) at Harvard.
Basic Qualifications	A Ph.D. in economics, management, psychology, sociology, or a related field. PLEASE NOTE: If you have obtained your Ph.D. in the past 12 months you must be able to provide a certificate of completion from the degree-granting institution OR a letter from the institute’s registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been conferred. No exceptions.
Additional Qualifications	Strong team player with excellent communication skills required.
Special Instructions	TO APPLY: PLEASE DO NOT APPLY ONLINE. Applications will be accepted until the position is filled. Please email the following lish@harvard.edu with subject “Postdoctoral Fellowship (Area of Interest)”. Note the Area of Interest should be either Online Platforms and Contests, Science of Science, the Economics and Management of AI systems, or Innovation for All. Curriculum vitae Copy of academic records (unofficial records are acceptable) 2-page description of relevant experience Two research papers Contact details of at least two references Candidates may be asked to undergo an assessment as part of the interview process. Only applicants who follow these instructions will be considered.
Contact Information	lish@harvard.edu
Contact Email	lish@harvard.edu
Equal Opportunity Employer	Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.
Minimum Number of References Required
Maximum Number of References Allowed
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